Using context to build semantics

Kwantes, Peter J.

doi:10.3758/bf03196761

Cited by 78 publications

(95 citation statements)

References 12 publications

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“…Composite trace models of memory are, generally, more limited than separate-trace models because the greater flexibility afforded by storage of separate traces allows separatetrace models to mimic the behavior of composite models (Nosofsky, 1988;Kwantes, 2005).…”

Section: Separate and Composite Representationsmentioning

confidence: 99%

A dynamic approach to recognition memory.

Cox

Shiffrin

2017

Psychological Review

105

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ii Acknowledgements First, I extend my thanks to my committee members for their time, consideration, and guidance. I would especially like to thank my advisors, Rich Shiffrin and Mike Jones, for first convincing me to come to Indiana, making me feel welcome, and guiding me into the world I now inhabit. I must also thank Isaiah Harbison, Michael Dougherty, and Sharona Atkins for introducing me to the very idea of mathematical psychology and for teaching me and trusting me. show how the dynamic model can be extended to account for paradigms like associative recognition and list discrimination, leading to another novel test of the presence of recall-like processes. Associative recognition, list discrimination, recognition of similar foils, and source exclusion are all better explained by the formation of a compound cue rather than recall, although source memory is found to be better modeled by a recall process.

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Section: Separate and Composite Representationsmentioning

confidence: 99%

A dynamic approach to recognition memory.

Cox

Shiffrin

2017

Psychological Review

105

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Section: Minerva-almentioning

confidence: 99%

“…Minerva 2 is an instance theory of human memory for explaining episodic recognition and judgment of frequency. The theory has since been applied to a wide range of memory phenomena (e.g., Arndt & Hirshman, 1998;Benjamin, 2010;Clark, 1997;Dougherty, Gettys, & Ogden, 1999;Goldinger, 1998;Hicks & Starns, 2006;Jamieson, Holmes & Mewhort 2010;Jamieson, Hannah & Crump 2010;Jamieson & Mewhort, 2009a, 2009b, 2010Kwantes, 2005;Kwantes & Mewhort, 1999;Kwantes & Neal, 2006;Thomas, Dougherty, Sprenger, & Harbison, 2008).Informally, Minerva 2 is a theoretic framework that describes the memorial processes involved in representing, storing, and retrieving instances of experience. The model assumes that each experience is stored in memory as a unique trace (i.e., an instance).…”

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confidence: 99%

“…However, discrepancy encoding allows feature values to range continuously between ±2. The remedy was to replace Minerva 2's similarity formula with a standard vector cosine (see Kwantes, 2005, for precedence):where P j is the value of the jth feature in the probe, M ij is the value of the jth feature of the ith row in memory, and n is the number of features in the cue-fields of the vectors under comparison. The cosine measure of similarity is conceptually consistent with the similarity measure used in the Minerva 2 model.…”

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confidence: 99%

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An instance theory of associative learning

2011

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We present and test an instance model of associative learning. The model, Minerva-AL, treats associative learning as cued recall. Memory preserves the events of individual trials in separate traces. A probe presented to memory contacts all traces in parallel and retrieves a weighted sum of the traces, a structure called the echo. Learning of a cue-outcome relationship is measured by the cue's ability to retrieve a target outcome. The theory predicts a number of associative learning phenomena, including acquisition, extinction, reacquisition, conditioned inhibition, external inhibition, latent inhibition, discrimination, generalization, blocking, overshadowing, overexpectation, superconditioning, recovery from blocking, recovery from overshadowing, recovery from overexpectation, backward blocking, backward conditioned inhibition, and second-order retrospective revaluation. We argue that associative learning is consistent with an instance-based approach to learning and memory.Keywords Associative learning . Memory . Instance theory . Exemplar theoryIn a simple associative learning procedure, a cue, A, is presented followed by an outcome, X. With experience, presentation of A elicits anticipation of X. The growth of anticipation is the process of associative learning.Typically, associative learning is modeled as a gradual accrual of excitatory and inhibitory connections between stimulus units. Associative strength is treated as summative; so the strength of an association between stimuli on trial t stands for the entire history of learning. This scheme for understanding learning is well described in the RescorlaWagner model (Rescorla & Wagner, 1972) and the many theories that follow from it.Whereas summative theories provide insight into learning, they make an unreasonable assumption. Namely, they deny that the learner remembers the events of separate learning trials-a problem that Miller, Barnet, and Grahame (1995) call the assumption of path independence. The problem is important. First, it distinguishes learning from memory. Second, it denies a growing body of evidence that animals other than humans remember the events of learning trials (e.g., Fagot & Cook, 2006;Vaughan & Greene, 1984;Voss, 2009).In contrast to the classical theories of learning, instance theories of human memory identify the individual experience (i.e., the instance) as the primitive unit of knowledge and treat learning as the accumulation and deployment of instances from memory. Brooks (1978Brooks ( , 1987 was among the first to champion the view. Medin and Schaffer (1978) were among the first to formalize it (see also Reed, 1972). Hintzman's (1986Hintzman's ( , 1988 Minerva 2 model and Nosofsky's (1986) generalized context model are classic formalizations of the view. Kruschke's (1992Kruschke's ( , 1996Kruschke's ( , 2001) and Logan's (1988Logan's ( , 2002 Learn Behav (2012) 40:61-82 DOI 10.3758/s13420-011-0046-2 In this article, we build on previous efforts (Jamieson, Hannah & Crump 2010) to show that an instance model of ass...

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“…This has the effect of estimating roughly how many, and to what degree, other words are "sufficiently" similar (by some encoding) to a target word across the lexicon. Furthermore, although a process model of LD and naming is beyond the scope of this article, the use of a threshold seems more cognitively plausible, since it restricts to a manageable size the subset of lexical memory against which a probe must be compared (indeed, such "activation" thresholds are often employed in memory models for that reason; see, e.g., Kwantes, 2005;Shiffrin & Steyvers, 1997). While the choice of a particular threshold of is somewhat arbitrary, any value greater than about 0.25 preserves the qualitative trends in the following analyses.…”

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confidence: 99%

Toward a scalable holographic word-form representation

et al. 2011

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Phenomena in a variety of verbal tasks-for example, masked priming, lexical decision, and word naming-are typically explained in terms of similarity between word-forms. Despite the apparent commonalities between these sets of phenomena, the representations and similarity measures used to account for them are not often related. To show how this gap might be bridged, we build on the work of Hannagan, Dupoux, and Christophe, Cognitive Science 35:79-118, (2011) to explore several methods of representing visual word-forms using holographic reduced representations and to evaluate them on their ability to account for a wide range of effects in masked form priming, as well as data from lexical decision and word naming. A representation that assumes that wordinternal letter groups are encoded relative to word-terminal letter groups is found to predict qualitative patterns in masked priming, as well as lexical decision and naming latencies. We then show how this representation can be integrated with the BEAGLE model of lexical semantics (Jones & Mewhort, Psychological Review 114:1-37, 2007) to enable the model to encompass a wider range of verbal tasks.

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Using context to build semantics

Cited by 78 publications

References 12 publications

A dynamic approach to recognition memory.

A dynamic approach to recognition memory.

An instance theory of associative learning

Toward a scalable holographic word-form representation

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